Multitrack performance scoring for assets comprising digital media

ABSTRACT

Systems and methods are provided for determining the efficacy of digital assets provided to an audience. One embodiment is a system that includes a brand management server. The brand management server includes a memory that stores assets comprising digital media. The brand management server also includes a controller. The controller distributes the assets for consumption by members of an audience at remote devices. The controller also selects one of the assets, determines a look-back period, acquires metrics indicating at least two different types of consumption of the asset by the audience during the look-back period, and calculates an asset score for the asset that indicates a popularity of the asset and is based on the metrics indicating the at least two different types of consumption.

FIELD

The disclosure relates to the field of provisioning digital content, andin particular, to scoring digital content based on the manner in whichthe digital content has been accessed and/or utilized by an audience.

BACKGROUND

Brand managers (e.g., a Chief Marketing Officer (CMO) of a company, adesigner, artist, etc.) help to ensure a desired level of publicawareness for one or more brands. For example, brand managers maycoordinate the distribution of assets (i.e., digital media such as avideo, image, text, audio, etc.) that advertise a brand's existence toan audience of potential customers. When the asset is consumed (e.g.,viewed) by members of the audience, public awareness of the brand may bebeneficially increased.

A brand manager may administer hundreds or thousands of assets for eachof multiple brands, and many assets within a brand may be highly similarto each other. For example, different assets may comprise differentresolutions of a video or image, a slightly altered version of a videoor image, etc. While it may be beneficial to have numerous assetsavailable, this may also cause a brand manager to encounter difficultywhen determining which asset is optimal for a given advertisingopportunity.

Further compounding this issue, the success of a brand is often judgedbased on the commercial success of associated products. However, salesdata for products is slow to retrieve, sales are impacted by otherfactors than advertising, and it may take time for changes inadvertising to alter consumer behavior in a manner that impacts sales.This means that for many brand managers, it may be difficult if notimpossible to determine the effectiveness of individual assets inimproving brand awareness.

Hence, those who manage brands continue to seek out enhanced systems andmethods for achieving these goals.

SUMMARY

Embodiments described herein provide brand management tools that arecapable of quantitatively assessing the worth of a given asset (an“asset score”), based on multiple types of metrics indicating how theasset is being consumed by an audience. This allows a brand manager torapidly determine which assets for a brand provide the greatest amountof value for a given audience. The techniques described herein alsoenable a brand manager to track an asset's score over time, to aggregatescores of assets to determine a value of an entire brand, and topredictively estimate scores for newly created assets. Thus, the systemsand methods described herein provide a benefit by enabling quantitativeanalysis and evaluation of asset consumption in a dynamic and holisticfashion.

One embodiment is a system that includes a brand management server. Thebrand management server includes a memory that stores assets comprisingdigital media. The brand management server also includes a controller.The controller distributes the assets for consumption by members of anaudience at remote devices. The controller also selects one of theassets, determines a look-back period, acquires metrics indicating atleast two different types of consumption of the asset by the audienceduring the look-back period, and calculates an asset score for the assetthat indicates a popularity of the asset and is based on the metricsindicating the at least two different types of consumption.

A further embodiment is a method. The method includes storing assetscomprising digital media, and distributing the assets for consumption bymembers of an audience at remote devices. The method also includesselecting one of the assets, determining a look-back period, acquiringmetrics indicating at least two different types of consumption of theasset by the audience during the look-back period, and calculating anasset score for the asset that indicates a popularity of the asset andis based on the metrics indicating the at least two different types ofconsumption.

Yet another embodiment is a non-transitory computer readable mediumembodying programmed instructions which, when executed by a processor,are operable for performing a method. The method includes storing assetscomprising digital media, and distributing the assets for consumption bymembers of an audience at remote devices. The method also includesselecting one of the assets, determining a look-back period, acquiringmetrics indicating at least two different types of consumption of theasset by the audience during the look-back period, and calculating anasset score for the asset that indicates a popularity of the asset andis based on the metrics indicating the at least two different types ofconsumption.

Other illustrative embodiments (e.g., methods and computer-readablemedia relating to the foregoing embodiments) may be described below. Thefeatures, functions, and advantages that have been discussed can beachieved independently in various embodiments or may be combined in yetother embodiments further details of which can be seen with reference tothe following description and drawings.

DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure are now described, by way ofexample only, and with reference to the accompanying drawings. The samereference number represents the same element or the same type of elementon all drawings.

FIG. 1 is a block diagram depicting an asset scoring system in anillustrative embodiment.

FIG. 2 is a flowchart illustrating a method of operating an assetscoring system in an illustrative embodiment.

FIG. 3 is a graph illustrating historical and projected scores for anasset in an illustrative embodiment.

FIG. 4 is a block diagram illustrating score prediction for assets in anillustrative embodiment.

FIG. 5 is a table illustrating a list of assets in an illustrativeembodiment.

FIG. 6 is a chart illustrating the estimated influence of image featuresupon asset scores in an illustrative embodiment.

FIG. 7 is a graph illustrating progress of an asset towards a predictedscore in an illustrative embodiment.

FIG. 8 is a block diagram illustrating inputs for brand scorecalculation in an illustrative embodiment.

FIG. 9 is a graph illustrating historical and projected scores forbrands in an illustrative embodiment.

FIG. 10 is a block diagram illustrating brand score calculation in anillustrative embodiment.

FIG. 11 depicts an illustrative computing system operable to executeprogrammed instructions embodied on a computer readable medium.

DESCRIPTION

The figures and the following description depict specific illustrativeembodiments of the disclosure. It will thus be appreciated that thoseskilled in the art will be able to devise various arrangements that,although not explicitly described or shown herein, embody the principlesof the disclosure and are included within the scope of the disclosure.Furthermore, any examples described herein are intended to aid inunderstanding the principles of the disclosure, and are to be construedas being without limitation to such specifically recited examples andconditions. As a result, the disclosure is not limited to the specificembodiments or examples described below, but by the claims and theirequivalents.

Systems and Components

FIG. 1 is a block diagram depicting an asset scoring system 100 in anillustrative embodiment. Asset scoring system 100 comprises any system,component, or device that is capable of scoring assets based on metricsindicating how those assets were consumed by an audience. Asset scoringsystem 100 may also distribute assets for consumption by an audience.

In this embodiment, asset scoring system 100 includes brand managementserver 110, which distributes assets to third-party servers 152 and/orremote devices 180 via network 140 (e.g., the Internet). Third-partyservers 152 may further distribute assets to audience members at remotedevices 180 via email campaign 162, social network campaign 164,cellular campaign 166, and/or Content Delivery Network (CDN) 168. Brandmanagement server 110 receives metrics indicating the consumption ofassets being distributed, and assigns scores to individual assets basedon these metrics. Assets 132-138 within brand management server 110 maybe accessed, modified, and/or updated based on input from a user (e.g.,a brand manager) operating client device 170.

Brand management server 110 comprises interface (UF) 112, controller114, and memory 116. I/F 112 may comprise any suitable physicalinterface for receiving data, such as an Ethernet interface, UniversalSerial Bus (USB) interface, an interface compliant with Institute ofElectrical and Electronics Engineers (IEEE) 802.11 protocols, etc.Controller 114 manages the operations of brand management server 110 indistributing and/or evaluating assets. Thus, controller 114 may selectassets for distribution, may determine which metrics of consumption tocollect from other entities (e.g. third-party servers 152 or remotedevices 180), may calculate asset scores, etc. Controller 114 may beimplemented, for example, as custom circuitry, as a hardware processorexecuting programmed instructions, or some combination thereof.

Memory 116 stores asset library 130, which comprises a collection ofassets for one or more brands. Asset library 130 may include asset 132(an image), asset 134 (a video), asset 136 (text), asset 138 (audio),etc. While only four assets are illustrated in FIG. 1, any suitablenumber of assets may be stored in asset library 130, and these assetsmay be categorized based on brand, shared features, etc. Memory 116 alsoincludes asset scoring model 124, which is utilized to determine theasset scores of individual assets based on asset metrics 126. Assetscoring model 124 may comprise, for example, a formula that appliesweights to various metrics of consumption, a machine learning model(e.g., neural network), etc. Memory 116 further stores brand scoringmodel 122. Brand scoring model 122 includes a formula or machinelearning model for calculating the score of a brand comprising a groupof assets. Memory 116 may be implemented as a digital storage devicesuch as a hard disk, flash memory, optical disc, etc.

Asset Score Calculation

Illustrative details of the operation of asset scoring system 100 willbe discussed with regard to FIG. 2. Assume, for this embodiment, that auser (e.g., a brand manager) has accessed brand management server 110via client device 170, and has uploaded a set of assets fordistribution. For example, the assets may all be related to the samebrand.

FIG. 2 is a flowchart illustrating a method 200 for operating an assetscoring system in an illustrative embodiment. The steps of method 200are described with reference to asset scoring system 100 of FIG. 1, butthose skilled in the art will appreciate that method 200 may beperformed in other systems. The steps of the flowcharts described hereinare not all inclusive and may include other steps not shown. The stepsdescribed herein may also be performed in an alternative order.

In step 202, controller 114 stores the assets from the user in assetlibrary 130. Each of the assets comprises one or more pieces of digitalmedia (e.g., a video, an image, a sound file, a piece of text). Assetsstored in asset library 130 may be accompanied by metadata indicating ascore for the asset, a size of the asset, a resolution of the asset, acolor space of the asset, etc.

Controller 114 receives input from the user at client device 170indicating preferred techniques for distributing the assets. Forexample, the user input may indicate one or more third-party servers toreceive copies of the assets. Based on this input, controller 114distributes the assets for consumption by members of the audience (e.g.,website users, social media users, cellular device users, etc.). Theassets may be distributed directly, via third-party servers 152, and/orremote devices 180 in step 204. For example, an asset may be directlydistributed to remote devices 180 of audience members by brandmanagement server 110 (e.g., as part of a website hosted at brandmanagement server 110). Alternatively, brand management server 110 maydistribute an asset to a third-party server 152, which may then providecopies of the asset to remote devices 180 of audience members.

Brand management server 110 continues to distribute the assets over aperiod of time. Eventually, controller 114 determines that the time hascome to score at least one of the assets being distributed. To this end,controller 114 selects one of the assets in step 206. The asset may beselected based on any criteria, such as in accordance with a predefinedlist, based on user input, etc. In one embodiment, all assets within abrand are selected for batch processing on a periodic basis (e.g.,daily).

With an asset selected, controller 114 determines a look-back period instep 208. The look-back period may vary depending on the asset, or maybe constant for all assets. In one embodiment, the look-back periodcomprises a predefined length of time that advances forward as afunction of time (e.g., the last week, last day, last month, last hour,etc.).

In step 210, controller 114 acquires metrics indicating at least twodifferent types of consumption of the asset by the audience during thelook-back period. As used herein, a “type of consumption” refers to aprocess by which an audience member digitally interacts with an asset.Measurements of particular types of consumption include a count ofviews, a count of downloads, a count of shares, a count of clicks, acount of conversions or value of conversions, a count of likes, a countof searches, a count of comments, and/or a count of tags used byaudience members in relation to the asset. In instances where brandmanagement server 110 is directly distributing assets to members of theaudience (e.g., via network 140), metrics may be directly determined andaccumulated by controller 114 based on interactions between brandmanagement server 110 and remote devices 180. In instances where anasset is hosted on a third-party server 152, such servers may notifybrand management server 110 on a periodic basis as the third-partyservers 152 acquire the metrics. In one embodiment, brand managementserver 110 operates a publish/subscribe (“pub/sub”) server that receivesupdates from the third-party servers 152 via a backend data pipelinethat has an event-ingest service. Events such as the consumption of anasset may be received at the pub/sub server from various sources, andthese events may be pushed a publish-subscribe pipeline accessed bybrand management server 110.

With the metrics acquired, in step 212 controller 114 calculates anasset score for the asset that indicates a popularity of the asset andis based on the metrics indicating the at least two different types ofconsumption. In one embodiment, controller 114 generates an asset scorefor each asset being actively distributed, by performing steps 210-212at regular intervals for a large group of assets.

An asset score is calculated using asset scoring model 124, and theasset score may comprise a time-weighted combination of the metrics foreach of the at least two different types of consumption. For example,each act of consumption of the asset during the look-back period may beassigned a value depending on the type of consumption, and the valuecontributed to an asset score by an act of consumption may progressivelybe degraded by a time decay function, based on a length of time ago thatthe act of consumption occurred.

In one embodiment, data received via a pipeline is aggregated on a dailybasis and used by controller 114 to calculate asset scores. As a part ofcalculating asset scores, a “raw asset score” for each asset iscomputed. In this embodiment, the raw asset score is a time weightedcombination of counts of each of the different types of consumption asper formula (1) below, wherein RS_(i) is the raw score for an asset i,w_(k) are the weights for each type of consumption k, g (t) is a timedecay function (e.g., an exponential function, step function, linearfunction, etc.), and C_(i) ^(k)(t) is the count of a type of consumptionk for asset i on day t:

RS _(i)=Σ_(t)Σ_(k) w _(k) g(t)C _(i) ^(k)(t)   (1)

Having computed raw asset scores, controller 114 may further normalizethe asset scores within a range (e.g., between zero and one hundred).This may be performed by selecting a group of assets for normalization(e.g., assets within the same brand, a group of similar images, assetsthat are text, assets that are images, assets that are video, assetsacross an entire organization, etc.), and identifying an asset withinthe group having a highest raw asset score. The asset with the highestraw score may be referred to as max(RS₁, . . . , RS_(n)). Normalizedasset scores may then be determined within a desired range from zero toa maximum value (e.g., one hundred) from each raw score RS_(i) accordingto formula (2) below:

$\begin{matrix}{S_{i} = {\frac{RS_{i}}{\max\left( {{RS_{1}},{.\;.\;.}\;,{RS}_{n}} \right)} \times \left( {{Maximum}\mspace{14mu}{Value}} \right)}} & (2)\end{matrix}$

The weights w_(k) assigned to each type of consumption, and the timedecay function g(t), may be predefined or may be based on other factors.For example, weights for different types of consumption may be equal bydefault. In one embodiment, a user chooses weights for each type ofconsumption. For example, a user may define weights for different typesof consumption using natural language descriptors (e.g., “veryimportant,” “unimportant,” “mildly important,” etc.). Each naturallanguage descriptor for a weight may be associated with a differentvalue (e.g., 0.9, 0.1, 0.4, etc.). In a similar fashion, the user maychoose a type of decay function g(t) (e.g., none, exponential decay,step function, or gradual decay), and controller 114 may then implementa predefined decay function of the selected type. In furtherembodiments, the user may even specifically define the contents of adecay function.

With the normalized asset scores known, controller 114 provides thenormalized asset scores to client device 170. Client device 170 may thenpresent the user with a Graphical User Interface (GUI) depicting thenormalized asset scores, and may arrange and color code the assets basedon their scores.

Method 200 provides a substantial advantage over prior techniques,because it enables input from a variety of sources (indicating a varietyof types of consumption of an asset), to be aggregated into a singlevalue that is easily comparable against other assets and is easilyinterpretable by a human. This helps a brand manager to rapidly identifyassets which are underperforming, and therefore addresses technicalproblems related to processing and presenting information in a mannerthat facilitates interpretation and understanding by users.

In further embodiments, weights initially chosen by a user may befine-tuned by controller 114 based on a machine learning model (e.g., aneural network) stored in memory 116. The machine learning model maychange the weights assigned to each different type of consumption basedon feedback from the user over time. More specifically, once everyperiod of time (e.g., once per week), controller 114 may query the userto determine whether the asset score for an asset or group of assetsappears to be realistic to the user. The user may then provide a naturallanguage descriptor of performance (e.g., “perfect,” “totally-off,” or“somewhat relevant”), or a numeric score. The machine learning model maythen use this feedback as input in order to adjust the currentcombination of weights. Controller 114 may even use the feedback fromthe user to build a model correlating weights and relevancy as performula (3) below:

relevancy=f(w ₁ , . . . , w _(n))   (3)

The model that correlates weights with relevancy may be implemented as astandard classification model (e.g., a logistic-regression model thatcorrelates weights with the textual descriptors discussed above). Havingdetermined a relationship between relevancy and weights, variouscombination of weights may be applied to the machine learning modeluntil a combination of weights that maximizes relevancy has beendetermined (e.g., in order to solve an unconstrained optimizationproblem). The weights determined from this procedure may then be used toprovide more relevant asset scores to the user. These relationshipsbetween relevancy and weights may vary on a user-by-user basis, andcontroller 114 may therefore determine different relationships fordifferent users who manage different assets or brands.

With a discussion of asset score calculation provided above, furtherdiscussion focuses upon how asset scores may be tracked, predicted, andcombined in order to provide greater insight to brand managers.

Asset Score Tracking and Prediction

By calculating the asset scores discussed above on a periodic basis, thehistorical change in score for an asset may be tracked over a length oftime (e.g., a week, a month, a year, etc.). FIG. 3 is a graph 300illustrating asset scores over time in an illustrative embodiment. FIG.3 includes historical scores 310 (i.e., a historical series of assetscores), which are located within a historical region and indicatepreviously calculated scores for an asset. Based on historical scores310, controller 114 determines an expected range of values for assetscores, as indicated by boundaries 320. The expected range of values maycomprise, for example, a range occupied by a large percentage (e.g.,eighty percent, ninety five percent, ninety nine percent, two standarddeviations, etc.) of prior scores for the asset. The prior scores may beconsidered across the entire history of the asset, or for a predefinedperiod (e.g., one year). Using this technique, even though a dip 330 ora peak 340 outside of the expected range of values may exist within thehistorical scores 310, most scores will be located between boundaries320. Graph 300 may be presented to a user of client device 170 in orderto report how the asset has performed over time. In further embodiments,historical scores 310 for multiple assets may be presented on the samegraph.

Historical scores 310 may also be used in order to identify unexpectedchanges in the popularity of an asset over time. As a part of thisprocess, controller 114 may compare a recent asset score (e.g., the mostrecent asset score) to historical scores 310. In one embodiment, if anasset score leaves the expected range (an “asset score anomaly”), orincreases or decreases at a rate faster than its historical average (an“asset acceleration anomaly”) (e.g., twice as fast, ten times as fast),controller 114 may generate a notification to indicate this condition toa user at client device 170. Other types of asset anomaly may also bereported if an asset starts to behave in a markedly different mannerthan its normal baseline performance. For example, controller 114 maygenerate a report if the historical average score of the asset changesby more than a threshold amount (e.g., ten percent), if the score of theasset changes with more or less volatility than in the past, etc.

Graph 300 also includes a forecast region, wherein predicted scores 350for the asset are included. A predicted score 350 may comprise anestimate of an asset score at a known time in the future, and may bebased on historical fluctuations or changes in the score for an asset.The predicted scores 350 may be determined based on a linear regressionof a series of historical asset scores, may be based on an analysisperformed by a machine learning model (e.g., a trained deep neuralnetwork), etc. Predicted scores 350 are bounded by confidence limits360, which indicate a range of estimated scores within a standard erroror a predefined confidence interval (e.g., a ninety-five percentinterval). Confidence limits 360 are likely to increase in span overtime, as longer-range forecasts of scores are less likely to be accuratethan shorter-term forecasts.

New Asset Score Prediction

When a user generates a new asset and uploads the new asset to brandmanagement server 110, the user may desire to understand how the newasset will be received by its intended audience in comparison to otherexisting assets. To this end, controller 114 of brand management server110 is capable of generating predicted scores for the new asset, inorder to forecast how the audience will consume the new asset. This notonly provides immediate feedback regarding the quality of a new asset,but also provides an opportunity for the user to revise a new asset andimprove it, all before the audience has even viewed the new asset.

FIG. 4 is a block diagram 400 illustrating score prediction for assetsin an illustrative embodiment. According to FIG. 4, asset scores arepredicted for a new asset in real-time, based on an asset scoring model410 which considers features found within the new asset, features foundwithin existing assets, and scores for existing assets. The assetscoring model 410 may for example comprise a machine learning model,such as a neural network having layers with nodes that identify featuresfound within the new asset, and also having layers that predict thecontribution of each feature to the score of an asset. “Features”comprise any properties, content, or thematic similarities which may beshared between assets. Features may refer both to the file properties ofan asset (e.g., resolution, file type, etc.) and to objects or conceptsdepicted within (or referenced by) the contents of an asset (e.g., afeature of “tree” may be found if there is a depiction of a tree withinan image or video, a reference to a tree within text or audio, etc.).Features may therefore comprise file metadata, identifiable objectsdepicted within an image, a length of an audio or text file in time orwords, shared textual content in a text file, extracted word embeddingsof a text file, tags identified in an image file, convolution neuralnetwork encoding of an image (using transfer learning), an amount ofsaturation of an image, a primary hue of an image, etc.

As a preliminary matter when predicting the score of a new asset,controller 114 may select features to search for within new assets. Theselected features may be predefined based on user input, or may beautomatically determined. For example, L1/L2 regularization procedureslike lasso or ridge regression procedures in a linear regression modelmay be used to select features that are relevant and to weed outfeatures that are irrelevant. If a new asset shares selected featureswith other assets, then it may have a similar score to those otherassets.

Asset scoring model 410 may attempt to automatically determine whichfeatures exist within the new asset For example, asset scoring model 410may determine which features exist within an image, video, text file, oraudio file based on a KAZE descriptor in the OpenCV library, such asthat described in “KAZE Features” by Alcantarilla P. F., Bartoli A.,Davison A. J. (2012), in: Fitzgibbon A., Lazebnik S., Perona P., SatoY., Schmid C. (eds) Computer Vision—ECCV 2012. ECCV 2012, Lecture Notesin Computer Science, vol 7577, Springer, Berlin, Heidelberg. In afurther embodiment, controller 114 may receive data indicating whichfeatures that are known to exist within the new asset.

In some embodiments, it may be unclear whether certain features actuallyexist within an asset. Features may therefore be associated withconfidence values indicating their likelihood of existence within aspecific asset, and asset scoring model 410 may adjust its predictiveprocess based on these likelihoods.

With the number and type of shared features known, asset scoring model410 predicts a score for the new asset. Asset scoring model 410 mayutilize high fidelity models like neural network regression and randomforest regression techniques to predict asset scores and may also uselow fidelity techniques such as linear regression to identify a degreeof contribution of each feature to an asset score. For example, featurecorrelation may be determined based on learned parameters in a linearregression model within asset scoring model 410.

Asset scoring model 410 may be trained beforehand to ensure a desiredlevel of accuracy in predicting asset scores based on the selectedfeatures. Asset scoring model 410 may be updated periodically (e.g.,daily, weekly, monthly) as the scores 434 of assets 430 in training data420 change, in order to fine tune asset scoring model 410 and accountfor changes in audience taste or composition over time. Regardless ofthis fine-tuning process being performed on a periodic basis, controller114 may predict scores for new assets via asset scoring model 410 inreal time, based on currently trained parameters.

FIG. 5 is a table 500 illustrating a list of assets in an illustrativeembodiment. Table 500 illustrates how metadata for each image may bestored within asset library 130. According to FIG. 5, each entry 510 intable 500 identifies an asset by name, provides an asset scoredetermined for the asset, indicates a size of the asset in memory,dimensions of the asset, and depicted features found within the asset.Depicted features are listed as semicolon separated name-value pairs,wherein a name-value pair such as “BEACH:6” indicates with a numericalconfidence of six that a beach is depicted or represented within theasset. Each entry 510 may also indicate other types of metadata, such asan applied filter, a file type, Multipurpose Internet Mail Extensions(MIME) type, color palette, bit depth, rendering technique, backgroundcolor, compression algorithm, aperture of camera used to create theimage, contrast level of the image, primary hue of the image, lensinformation of the camera used to create the image, pixel information,saturation information, vibrance information, etc. Any and all of theproperties discussed above may be treated as features if desired.

FIG. 6 is a chart 600 illustrating estimated score contributions ofimage features in an illustrative embodiment. For example, chart 600 mayillustrate the value of weights for nodes within a neural network thatare each associated with a feature. Chart 600 illustrates that somefeatures may contribute positively to the score of an asset, while otherfeatures may contribute negatively. Chart 600 may also be provided to auser of client device 170 via a GUI, in order to suggest features toinclude in (or remove from) newly created assets.

FIG. 6 also illustrates that features which are often found together inassets may be grouped together, such as shown in group 610. The averageinfluence of features within a group may be reported in order to providefurther insights. For example, a group of features that depict a beachmay include features for sand, ocean, sun, and palm trees. The averagescore contribution of each feature in the group may then be reported toa user designing a new asset.

Assets themselves may also be grouped, using a clustering procedure suchas K-Means. Respective scores of grouped assets may then be compared inorder to provide context for the user. For example, users may bepresented with assets that share similar underlying features and whichbehave in a similar manner (e.g., with regard to score contribution overtime). This may help a user to determine which differences (if any) areresponsible for different scores between assets that share a largenumber of features.

With the predicted score of a new asset known, controller 114 maypresent information indicating the progress of the new asset towards itspredicted score. For example, FIG. 7 is a graph 700 illustratingprogress of an asset towards a predicted score 720 in an illustrativeembodiment. Graph 700, or a similar visual aid, may be presented bycontroller 114 to a user of client device 170 via a GUI. Graph 700presents historical scores 710 for a new asset since a point in time(e.g., since the new asset was first distributed), and includes apredicted score 720 indicating a predicted asset score for the new assetafter the new asset has been in distribution for at least a thresholdamount of time (e.g., a month, a week, any time long enough to fullymature, etc.). Controller 114 may report the difference between thecurrent score and the predicted score 720 of the asset, and may alsoforecast a date at which the new asset will reach its predicted score,using a linear regression or other forecasting technique based onhistorical scores for the new asset. In further embodiments, controller114 may modify an originally predicted score or originally forecastedscore using a Bayesian updating procedure (utilizing the new stream ofdata after that original prediction date). Adjusting these factorsprovides a better estimate of the remaining potential of an asset.

Brand Scoring and Prediction

While the above FIGS. and description focus upon determining andtracking scores for individual assets, the following FIGS. illustratescoring and tracking performed upon entire groups of assets (e.g., allassets within a brand) in order to arrive at an aggregate score for thegroup. Aggregate scores may help a user to determine the popularity ofone brand with respect to other brands, which in turn helps the user togauge market capture by different brands having a similar audience.These aggregate scores for groups of assets are also referred to hereinas “brand scores.”

FIG. 8 is a block diagram 800 illustrating inputs for brand scorecalculation in an illustrative embodiment. In this embodiment, inputsfor brand score calculation include social media feeds, asset scorescalculated as described above, inputs from an Application ProgrammingInterface (API) for brand management server 110, inputs from a CDN API,an advertiser (e.g., DoubleClick) API, other social media API, employeereview data pertaining to the brand, etc. These inputs are processed andused by controller 114 when implementing brand scoring model 850 (storedwithin memory 116) in order to arrive at a brand score. For example, inthe embodiment shown in FIG. 8, brand mention score engine 820 may parsesocial media feeds (e.g., Facebook posts, twitter posts, etc.) to detecta number of mentions of the brand (and/or assets for the brand) in orderto create a social media activity score.

Sentiment score engine 830 may perform natural language analysis uponsocial media feeds in order to determine whether mentions of a brand(and/or assets for the brand) are positive or negative. This may includedetermining whether the brand is associated with words having a positiveor negative connotation with respect to the brand. For example, wordsassociated with a raw or edgy tone may be considered positive for anenergy drinks brand, but may be considered negative for a brand ofmedications. The tone of various word combinations may be predefined bythe user, or may be determined by a trained machine learning model overtime. Individual sentiment determinations for an asset or brand may thenbe aggregated in order to arrive at a sentiment score. Each type ofscore may also be normalized with similar types of scores for brands inthe same vertical.

Brand asset score engine 840 reviews asset scores (e.g., raw assetscores) for a group of assets, for example by picking the highest rawscore within the group of assets or by summing these scores. The variousscoring engines discussed herein may be stored in memory 116 of brandmanagement server 110.

With the inputs received, controller 114 may operate brand scoring model850 in order to combine sentiment scores, mention scores, and/or assetscores into a raw brand score. Brand scoring model 850 may comprise apredefined formula, or a machine learning model such as a neural networkthat has been trained. A raw brand score may be normalized with regardto other brands in the same field (“vertical”) or other brands that aremarketed to the same demographic. These normalized brand scores (and/orbrand rankings) may then be presented to a user of client device 170 inorder to indicate a brand's position in relationship to other brands inits vertical (e.g., make-up, pharmaceuticals, alcoholic beverages,sporting goods, etc.). This not only illustrates the competitiveness ofa brand with regard to other brands, but also indicates whether a brandstill has the potential for growth relative to its competitors.

In one embodiment, the process of brand scoring is performed in thefollowing manner. Brand asset score engine 840 determines Raw BrandScores (RBFs) for each brand i within a predefined vertical. RBF_(i) maybe defined as the maximum value across all raw scores of assets in thebrand i according to formula (4) below:

RBF=max(RS ₁ ^(i) , . . . , RS _(n) ^(i))   (4)

RBF_(i) may alternatively be calculated as a summation of raw scores ofassets within a brand or other group of assets. In either case, RBF_(i)does not yet include brand mention scores or sentiment scores at thisjuncture.

RBF_(i) may be normalized with other brands belonging to the samevertical (e.g., apparel, sports, finance) according to formula (5)below, wherein BF_(i) is a normalized asset-based score for a brand,V_(i) is the vertical that brand i belongs, m is the total number ofbrands in the vertical, and Maximum Value is the maximum desirednormalized value for any normalized asset-based score for a brand:

BF _(i)=(Maximum Value)×RBF _(i)/max(RBF _(n) , . . . , RBF _(m)) forall {n, . . . , m} ∈ V _(i)   (5)

A finalized brand score may then be determined by combining a normalizedasset-based brand score with metrics from other sources (e.g., a socialmedia mention score, a social media sentiment score, etc.). The finalbrand score for each brand is computed as a weighted average of thevarious scores, including the normalized asset-based brand score,according to formula (6) below, wherein w_(j) is the weight assigned toeach type of score (e.g., asset-based brand score, sentiment scoreetc.), g(t) is a time decay function, and BS_(ij) the score for thei^(th) brand for the j^(th) type of score:

B _(i)=Σ_(t)Σ_(j) g(t)w _(j)BS_(ij)   (6)

Weights w_(j) used for the combination of different types of scores maybe determined as a function of user preference on a user-by-user basis,in a similar manner as weights for asset scores were determined above.This means that brand scores calculated for each brand may vary based onthe subjective preferences of each user (i.e., because users withdifferent preferences may weight different types of scores differently).

Current and previous trends related to a brand (e.g., stock prices for acompany that owns a brand, revenue for that company, etc.) may alsoimpact the weights w_(j) for a type of score j, and the degree of suchimpacts may again be tailored based on user preferences. Users may evenprovide additional scoring and metrics as desired to influence the scoreof a brand, and may track individual BS_(ij)values for different typesof score j for a brand i in order to receive immediate feedbackindicating which asset campaigns have the most impact on social media,the greatest degree of consumption by audience members, etc.

Types of Metrics Considered for Brand Scores

Metrics for a brand may include an almost limitless variety of inputs.For example, metrics may report a number of products related to thebrand, a number of employees at the company that owns the brand, anumber of employees that perform management of the brand, a number oflocations, cities, countries, and/or languages that the brand ismarketed in, a number of trademarks, patents, and/or copyrightsassociated with the brand, a number of publications describing thebrand, a number of lawsuits pending, won, or lost relating to the brand,a stock price of a company owning the brand, a number and/or popularityof press releases regarding the brand, demographics of audience members(reach), a number of years that the brand or the company operating thebrand has been in business, whether a brand is business-to-business orbusiness-to-consumer (which may influence how other metrics areweighted), etc.

Metrics may also indicate online presence related to a brand. Forexample, metrics for social media presence may report a number of socialmedia accounts owned by the brand, a number of followers for the brandon each of multiple social media platforms, an amount of retweets orreposts of brand names or assets, a sentiment score for posts that havebeen referred to by audience members, etc. Metrics of online activitymay report an ad conversion rate, recurrence of search terms related tothe brand in Google search trends (and associated sentiment), websiteusage (e.g., a number of monthly active users at a website for thebrand), online sales and revenue, a number of customer emails registeredto receive updates for the brand, a number of registered users at awebsite for the brand), a Search Engine Optimization (SEO) rating,and/or a score for security at a website for the brand.

Further metrics may indicate how actively the brand updates and managesits presence via assets. Such metrics may report a number of assets fora brand, sizes and/or resolutions for the assets, an average age of theassets, a rate of change in assets over time (“freshness”), etc.

Metrics may also report the reputation of a brand separately from thepopularity of the brand. These metrics may indicate a reputation ornumber of years of experience of executives for the brand, and/or areputation of affiliates for the brand. These metrics may even indicatean overall “ethical” representation of a brand, by providing reportsabout work environment, brand environmentalism, community involvement orcharity acts performed in relation to the brand, donations made byowners of the brand, and a level of transparency of the brand. Brandreputation may also be indicated by metrics reporting open sourcecontributions related to a brand, or a reputation or number of questionsanswered on StackOverflow relating to the brand.

Customer loyalty and attachment to a brand may also be reported viametrics. such metrics may report a rate of recurring customers ascompared to new customers, whether or not a brand has a credit card orreward program (and if so, a measurement of use of such credit cards orrewards programs), etc. Metrics in the form of surveys provided toaudience members may also indicate customer loyalty and attachment. Suchsurveys may ask whether an audience member has purchased a productassociated with the brand, a likelihood of the audience memberpurchasing another product associated with the brand, a likelihood ofproviding referrals, an amount of trust that the audience member has inthe brand, whether or not customer service experiences related to thebrand have been positive, and more.

Existing Brand Score Tracking and Prediction

Utilizing the brand score calculation techniques discussed above, thehistorical change in score for a brand may be tracked, and predictedbrand scores may be forecasted based on historical scores for a brandover a length of time (e.g., a week, a month, a year, etc.) in a similarmanner as described for assets in FIG. 3 above. For example, FIG. 9 is agraph 900 illustrating historical and projected brand scores in anillustrative embodiment. FIG. 9 includes historical brand scores 910,boundaries 920, a dip 930, a peak 940, predicted brand scores 950, andconfidence limits 960 for predicted brand scores 950. Anomalies for abrand may also be detected and reported in a similar manner as anomaliesfor an asset, based on changes in brand score over time.

With historical scores known for a brand or asset, future scores may beforecasted using any suitable time series techniques, such asAutoRegressive-Moving-Average (ARMA) models. Forecasts may also becomputed as a function of not only time but also various externalregression parameters such as reports of economic outlook for a companythat owns a brand, stock prices for the company, etc. Such forecasts maybe modeled using time-series regression extension models such asAutoregressive Integrated Moving Average with Explanatory Variable(ARIMAX) Model. This information may help a brand manager to quickly andaccurately predict the future performance of the brand over time.

The success of a brand may also be evaluated by generating an indexindicating an average of the brand's score over all days in a period oftime (e.g., week, month, quarter, year, etc.). Such an index may bedetermined according to formula (7) below, wherein BFIndex_(i) indicatesthe index value of a given brand i,j is equal to the selected period oftime (e.g., a specific month, week, etc.), ndays_(j) equals the numberof days in the period, k is an iterating value indicating a specificday, and B_(i) ^(k) indicates a brand score (e.g., raw or normalized) ofa brand i on a given day k:

$\begin{matrix}{{{BFIndex}_{i}\left( {{month} = j} \right)} = {\frac{1}{ndays_{j}}{\sum_{k}B_{i}^{k}}}} & (7)\end{matrix}$

The index values discussed above may also be normalized with otherbrands, for example, by grouping and normalizing brands that are withinthe same vertical.

FIG. 10 is a block diagram illustrating brand score prediction in anillustrative embodiment. Brand score predictions (e.g., for newlycreated brands) may be performed in a similar manner to asset scorepredictions. For example, brand scoring model 850 may predict orotherwise calculate brand scores in real-time, based on asset metrics1032, asset scores 1034, and brand metrics 1036 for one or more brands1030 indicated in training data 1020. Weights within brand scoring model850 may also be periodically updated based on training data 1020.

The calculations of asset scores and brand scores described herein (andpredictions relating thereto) may utilize a great deal of normalization,which may be processing intensive and may also rely on data which isonly periodically updated. In order to provide real-time asset scores orbrand scores, a machine learning serving model such as brand scoringmodel 850 or asset scoring model 410 may be pretrained to provide bestestimates of changes to scores in real-time.

As a part of the pretraining process, when asset scores or brand scoresare computed for each day in a batch process (e.g., a massively parallelcompute process), they are computed for various combinations of weightswhich are determined by a sampling algorithm such as a latin hypercubesampling procedure. The resulting asset scores calculated for variousweights are then used to build a machine learning regression model, suchas a deep neural network. The model may be pretrained to understandrelationships between asset scores and weights, based on algorithms suchas polynomial regression. This means that when a user changes thecombination of weights used to score an asset or brand, or when a userasks for a different weight combination, the pretrained model is capableof approximating scores for the assets at the new weights, withouthaving to re-normalize all asset scores and/or brand scores. Meanwhile,controller 114 may also trigger a background process that preciselycomputes new scores, and replaces the approximated scores at a latertime (e.g., after batch processing has completed at the end of a day andre-normalized asset and/or brand scores have been determined).

EXAMPLES

In the following examples, additional processes, systems, and methodsare described in the context of a brand management server that activelytracks and reports asset scores and brand scores.

In this example, a user at client device 170 has established a newaccount for a beer brand at brand management server 110. The useruploads multiple assets in the form of images to memory 116 of brandmanagement server 110, and instructs brand management server 110 to hostthe assets for distribution in a brewery website, and also to distributethe assets to a third-party server 152 for use in a social networkcampaign. Over a period of days, the assets are distributed and consumedby an audience. During this time, brand management server 110 acquiresdata from third-party server 152 via a publish/subscribe server. Thedata indicates consumption of the assets in the social network. Brandmanagement server 110 also accumulates data indicating consumption ofthe assets by audience members (e.g., potential customers) visiting thebrewery web site. In this example, the types of consumption of an assetinclude a count of views of the asset and a count of clicks on theasset.

After a week has passed, the user logs in to brand management server 110and requests that each asset in the brand be scored. Controller 114proceeds to calculate an asset score for each asset, by performing aweighted combination of clicks and views over the last week. Clicks areweighted ten times more heavily than views, and the amount contributedto asset score by a click or view degrades in a linear fashion over aperiod of seven days. Using this model, controller 114 generates a rawscore for each asset. Controller 114 then identifies the asset havingthe highest score, and normalizes all asset scores to a range betweenzero and ten based on the highest score. Assets are then sorted based onscore from highest to lowest, and are presented to the user for review.The user realizes that one of the assets has a score of only one, incomparison with other that range in score from five to ten. The userthen discontinues the underperforming asset.

In order to replace the underperforming asset, the user generates a newasset comprising a new image. The user then queries the controller 114to predict a score of the new asset. Controller 114 operates assetscoring model 410 in order to predict the score. Asset scoring model 410indicates that assets which are high resolution, have a high degree ofsaturation, and depict six-packs of beer are correlated with higherscores. Meanwhile, asset scoring model 410 indicates that assets whichare dark and have substantial amounts of text are correlated with lowerscores. Using this information, controller 114 determines that the newasset is likely to have an asset score of three point eight. The userdetermines that the asset includes a large amount of text, removes thetext, and re-submits the new asset. Controller 114 then arrives at apredicted score of eight point one. The user then submits the new assetfor distribution.

While the new asset is being distributed, controller 114 determines adifference between the current score and the predicted score of theasset, and reports this information to the user. The user tracks thescore of the asset over a period of ten days, at which time the assethas achieved its predicted score of eight point one.

The user also requests that brand management server 110 provide a scorefor the beer brand as a whole, compared to other brands that marketalcoholic beverages. Controller 114 therefore proceeds to review metricsfor the brand to calculate a raw brand mention score, raw brandsentiment score, and raw brand score. Controller 114 normalizes the rawscores based on raw scores for other brands that market alcoholicbeverages, and performs a weighted combination of the normalized scoresfor the beer brand to arrive at a normalized brand score. Controller 114reports a normalized brand score of five point eight out of ten for thebeer brand. The user then proceeds to use this information to inform thecreation of further assets and the budget used for advertisingcampaigns.

Controller 114 also publishes the brand score for presentation toaudience members, if the user has approved this action. Publiclyavailable rankings brands based on goodness, effectiveness, workenvironments, etc. may help to further increase a brand's popularitywith its audience.

Any of the various computing and/or control elements shown in thefigures or described herein may be implemented as hardware, as aprocessor implementing software or firmware, or some combination ofthese. For example, an element may be implemented as dedicated hardware.Dedicated hardware elements may be referred to as “processors”,“controllers”, or some similar terminology. When provided by aprocessor, the functions may be provided by a single dedicatedprocessor, by a single shared processor, or by a plurality of individualprocessors, some of which may be shared. Moreover, explicit use of theterm “processor” or “controller” should not be construed to referexclusively to hardware capable of executing software, and mayimplicitly include, without limitation, digital signal processor (DSP)hardware, a network processor, application specific integrated circuit(ASIC) or other circuitry, field programmable gate array (FPGA), readonly memory (ROM) for storing software, random access memory (RAM),non-volatile storage, logic, or some other physical hardware componentor module.

In one particular embodiment, instructions stored on a computer readablemedium direct a computing system of brand management server 110, toperform the various operations disclosed herein. FIG. 11 depicts anillustrative computing system 1100 operable to execute a computerreadable medium embodying programmed instructions. Computing system 1100is operable to perform the above operations by executing programmedinstructions tangibly embodied on computer readable storage medium 1112.In this regard, embodiments may utilize instructions (e.g., code)accessible via computer-readable medium 1112 for use by computing system1100 or any other instruction execution system. For the purposes of thisdescription, computer readable medium 1112 comprises any physical mediathat is capable of storing a program for use by computing system 1100.For example, computer-readable medium 1112 may be an electronic,magnetic, optical, electromagnetic, infrared, semiconductor device, orother non-transitory medium. Examples of computer-readable medium 1112include a solid state memory, a magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk, and an optical disk. Current examples of opticaldisks include Compact Disk-Read Only Memory (CD-ROM), CompactDisk-Read/Write (CD-R/W), Digital Video Disc (DVD), and Blu-Ray Disc.

Computing system 1100, which stores and/or executes the instructions,includes at least one processor 1102 coupled to program and data memory1104 through a system bus 1150. Program and data memory 1104 includelocal memory employed during actual execution of the program code, bulkstorage, and/or cache memories that provide temporary storage of atleast some program code and/or data in order to reduce the number oftimes the code and/or data are retrieved from bulk storage (e.g., aspinning disk hard drive) during execution.

Input/output or I/O devices 1106 (including but not limited tokeyboards, displays, touchscreens, microphones, pointing devices, etc.)may be coupled either directly or through intervening I/O controllers.Network adapter interfaces 1108 may also be integrated with the systemto enable computing system 1100 to become coupled to other computingsystems or storage devices through intervening private or publicnetworks. Network adapter interfaces 1108 may be implemented as modems,cable modems, Small Computer System Interface (SCSI) devices, FibreChannel devices, Ethernet cards, wireless adapters, etc. Display deviceinterface 1110 may be integrated with the system to interface to one ormore display devices, such as screens for presentation of data generatedby processor 1102.

What is claimed is:
 1. A system comprising: a brand management servercomprising: a memory that stores videos; and a controller thatdistributes the videos via an interface coupled for communication with anetwork for consumption by members of an audience at remote devices, foreach of the videos, the controller automatically determines a look-backperiod, acquires metrics indicating at least two different types ofconsumption of the video by the audience during the look-back period,and calculates an asset score for the video that indicates a popularityof the video and is based on the metrics indicating the at least twodifferent types of consumption, wherein the asset score comprises aweighted combination of the metrics of the at least two different typesof consumption, and each of the metrics has its own weight, whereinasset scores for videos in different brands are calculated concurrentlyin real-time using different weighted combinations of the metrics, thecontroller presents asset scores via a Graphical User Interface (GUI)for review by a user, wherein the controller arranges and color codesthe GUI based on the asset scores, and the controller stores the assetscores in the memory as metadata for the videos.
 2. The system of claim1 wherein: the controller determines an expected range of asset scoresfor the video based on historical asset scores for the video, whereinthe expected range comprises a range occupied by more than half of priorasset scores for the video during a predefined period, and thecontroller determines that a current asset score for the video is notwithin the expected range, and generates a notification in response todetermining that the current asset score is not within the expectedrange.
 3. The system of claim 1 wherein: the at least two differenttypes of consumption are selected from the group consisting of: views,downloads, shares, clicks, conversion, likes, searches, comments, andtags used by audience members in relation to the video; and the metricsare selected from the group consisting of: a count of views during thelook-back period, a count of downloads during the look-back period, acount of shares during the look-back period, a count of clicks duringthe look-back period, a count of conversions during the look-backperiod, a value of conversions during the look-back period, a count oflikes during the look-back period, and a count of searches during thelook-back period.
 4. The system of claim 1 wherein: the videos arestored in a library that indicates depicted features for each video, andthe features are selected from the group consisting of: a size of thevideo in memory, dimensions of the video, and depicted features foundwithin the video, an applied filter, a file type, Multipurpose InternetMail Extensions (MIME) type, color palette, bit depth, renderingtechnique, background color, compression algorithm, aperture of a cameraused to create the video, contrast level of the video, primary hue ofthe video, lens information of a camera used to create the video, pixelinformation, saturation information, and vibrance information, and thecontroller generates a chart indicating estimated contributions offeatures to asset scores.
 5. The system of claim 1 wherein: thecontroller identifies groups of videos that are within the same brand,and normalizes asset scores within each of the groups to be within adesired range of values between zero and a maximum value, by, for eachgroup: selecting the video in the group having a highest asset score,and for each video in the group, multiplying the asset score by themaximum value and dividing by the highest asset score.
 6. The system ofclaim 1 wherein: the controller calculates a historical series of assetscores for the video over time, forecasts asset scores for the video inthe future based on the historical series of asset scores, determinesconfidence limits which indicate a range of estimated asset scoreswithin a predefined confidence interval, and presents the range ofestimated asset scores and confidence limits within a chart displayed atthe GUI, wherein the confidence limits each depict a predefinedconfidence interval from an estimated asset score at a point in thefuture, and the chart increases a span of the confidence limits as arange of the forecast increases.
 7. The system of claim 1 wherein: thecontroller operates a machine learning model to predict asset scores forvideos, the machine learning model comprising a neural network havinglayers with nodes that identify features found within videos, and alsohaving layers that predict the contribution of each feature to the assetscore of a video.
 8. The system of claim 1 wherein: the controllerreceives a new video, identifies at least one feature depicted withinthe new video that is also depicted in other videos having known assetscores, and predicts an asset score for the new video based on the atleast one feature that is also depicted in other videos and furtherbased on a regression model that identifies a degree of contribution ofthe at least one feature to asset scores.
 9. A method comprising:storing videos in a memory; distributing the videos via an interfacecoupled for communication with a network for consumption by members ofan audience at remote devices; for each of the videos: determining alook-back period; acquiring metrics indicating at least two differenttypes of consumption of the video by the audience during the look-backperiod; and calculating an asset score for the video that indicates apopularity of the video and is based on the metrics indicating the atleast two different types of consumption, wherein the asset scorecomprises a weighted combination of the metrics of the at least twodifferent types of consumption, and each of the metrics has its ownweight, and wherein asset scores for videos in different brands arecalculated concurrently in real-time using different weightedcombinations of the metrics; presenting asset scores via a GraphicalUser Interface (GUI) for review by a user, wherein the GUI is arrangedand color coded based on the asset scores; and storing the asset scoresin the memory as metadata for the videos.
 10. The method of claim 9further comprising: determining an expected range of asset scores forthe video based on historical asset scores for the video, wherein theexpected range comprises a range occupied by more than half of priorasset scores for the video during a predefined period; determining thata current asset score for the video is not within the expected range;and generating a notification in response to determining that thecurrent asset score is not within the expected range.
 11. The method ofclaim 9 wherein: the at least two different types of consumption areselected from the group consisting of: views, downloads, shares, clicks,conversion, likes, searches, comments, and tags used by audience membersin relation to the video, and the metrics are selected from the groupconsisting of: a count of views during the look-back period, a count ofdownloads during the look-back period, a count of shares during thelook-back period, a count of clicks during the look-back period, a countof conversions during the look-back period, a value of conversionsduring the look-back period, a count of likes during the look-backperiod, and a count of searches during the look-back period.
 12. Themethod of claim 9 wherein: the videos are stored in a library thatindicates depicted features for each video, and the features areselected from the group consisting of: a size of the video in memory,dimensions of the video, and depicted features found within the video,an applied filter, a file type, Multipurpose Internet Mail Extensions(MIME) type, color palette, bit depth, rendering technique, backgroundcolor, compression algorithm, aperture of a camera used to create thevideo, contrast level of the video, primary hue of the video, lensinformation of a camera used to create the video, pixel information,saturation information, and vibrance information, and the method furthercomprises generating a chart indicating estimated contributions offeatures to asset scores.
 13. The method of claim 9 further comprising:identifying groups of videos that are within the same brand; andnormalizing asset scores within each of the groups to be within adesired range of values between zero and a maximum value, by, for eachgroup: selecting the video in the group having a highest asset score,and for each video in the group, multiplying the asset score by themaximum value and dividing by the highest asset score.
 14. The method ofclaim 9 further comprising: calculating a historical series of assetscores for the video over time; forecasting asset scores for the videoin the future based on the historical series of asset scores;determining confidence limits which indicate a range of estimated assetscores within a predefined confidence interval; presenting the range ofestimated asset scores and confidence limits within a chart displayed atthe GUI, wherein the confidence limits each depict a predefinedconfidence interval from an estimated asset score at a point in thefuture, and the chart increases a span of the confidence limits as arange of the forecast increases; and increasing a span of the confidencelimits as a range of the forecast increases.
 15. The method of claim 9further comprising: operating a machine learning model to predict assetscores for videos, the machine learning model comprising a neuralnetwork having layers with nodes that identify features found withinvideos, and also having layers that predict the contribution of eachfeature to the score of a video.
 16. The method of claim 9 furthercomprising: receiving a new video; identifying at least one featuredepicted within the new video that is also depicted in other assetshaving known asset scores; and predicting an asset score for the newvideo based on the at least one feature that is also depicted in othervideos and further based on a regression model that identifies a degreeof contribution of the at least one feature to asset scores.
 17. Anon-transitory computer readable medium embodying programmedinstructions which, when executed by a processor, are operable forperforming a method comprising: storing videos in a memory; distributingthe videos via an interface coupled for communication with a network forconsumption by members of an audience at remote devices; for each of thevideos: determining a look-back period; acquiring metrics indicating atleast two different types of consumption of the video by the audienceduring the look-back period; and calculating an asset score for thevideo that indicates a popularity of the video and is based on themetrics indicating the at least two different types of consumption,wherein the asset score comprises a weighted combination of the metricsof the at least two different types of consumption, and each of themetrics has its own weight, and wherein asset scores for videos indifferent brands are calculated concurrently in real-time usingdifferent weighted combinations of the metrics; presenting asset scoresvia a Graphical User Interface (GUI) for review by a user, wherein theGUI is arranged and color coded based on the asset scores; and storingthe asset scores in the memory as metadata for the videos.
 18. Themedium of claim 17 wherein the method further comprises: determining anexpected range of asset scores for the video based on historical assetscores for the video, wherein the expected range comprises a rangeoccupied by more than half of prior asset scores for the video during apredefined period; determining that a current asset score for the videois not within the expected range; and generating a notification inresponse to determining that the current asset score is not within theexpected range.
 19. The medium of claim 17 wherein: the at least twodifferent types of consumption are selected from the group consistingof: views, downloads, shares, clicks, conversion, likes, searches,comments, and tags used by audience members in relation to the video,and the metrics are selected from the group consisting of: a count ofviews during the look-back period, a count of downloads during thelook-back period, a count of shares during the look-back period, a countof clicks during the look-back period, a count of conversions during thelook-back period, a value of conversions during the look-back period, acount of likes during the look-back period, and a count of searchesduring the look-back period.
 20. The medium of claim 17 furthercomprising: the videos are stored in a library that indicates depictedfeatures for each video, and the features are selected from the groupconsisting of: a size of the video in memory, dimensions of the video,and depicted features found within the video, an applied filter, a filetype, Multipurpose Internet Mail Extensions (MIME) type, color palette,bit depth, rendering technique, background color, compression algorithm,aperture of a camera used to create the video, contrast level of thevideo, primary hue of the video, lens information of a camera used tocreate the video, pixel information, saturation information, andvibrance information, and the method further comprises generating achart indicating estimated contributions of features to asset scores.21. The medium of claim 17 further comprising: identifying groups ofvideos that are within the same brand; and normalizing asset scoreswithin each of the groups to be within a desired range of values betweenzero and a maximum value, by, for each group: selecting the video in thegroup having a highest asset score, and for each video in the group,multiplying the asset score by the maximum value and dividing by thehighest asset score.
 22. The medium of claim 17, wherein the methodfurther comprises: calculating a historical series of asset scores forthe video over time; forecasting asset scores for the video in thefuture based on the historical series of asset scores; determiningconfidence limits which indicate a range of estimated asset scoreswithin a predefined confidence interval; presenting the range ofestimated asset scores and confidence limits within a chart displayed atthe GUI, wherein the confidence limits each depict a predefinedconfidence interval from an estimated asset score at a point in thefuture, and the chart increases a span of the confidence limits as arange of the forecast increases; and increasing a span of the confidencelimits as a range of the forecast increases.
 23. The medium of claim 17,wherein the method further comprises: operating a machine learning modelto predict asset scores for videos, the machine learning modelcomprising a neural network having layers with nodes that identifyfeatures found within videos, and also having layers that predict thecontribution of each feature to the score of a video.
 24. The medium ofclaim 17, wherein the method further comprises: receiving a new video;identifying at least one feature depicted within the new video that isalso depicted in other assets having known asset scores; and predictingan asset score for the new video based on the at least one feature thatis also depicted in other videos and further based on a regression modelthat identifies a degree of contribution of the at least one feature toasset scores.